UC San Diego Researchers Use AI to Predict Opioid Addiction in High-Risk Patients

Researchers at the University of California San Diego School of Medicine are developing an AI model to predict opioid addiction in high-risk patients. The project, led by Dr. Rodney Gabriel, is funded by Wellcome Leap as part of a $50 million initiative called Untangling Addiction. The AI model will use generative artificial intelligence (GenAI) to predict addiction risks based on patterns from large patient datasets. The ultimate goal is to create a commercially available genomic and microbiome panel for clinicians to assess opioid addiction risk, and to integrate AI into electronic health record systems for real-time risk predictions.

AI Model to Predict Opioid Addiction in High-Risk Patients

Researchers at the University of California San Diego School of Medicine are developing an artificial intelligence (AI) model to predict opioid addiction in high-risk patients. Opioids, a group of potent pain-relieving drugs, have a high potential for addiction. In 2017, the opioid crisis was declared a national public health emergency by the U.S. Department of Health and Human Services. The AI model aims to identify patients most at risk of opioid addiction and provide resources to manage their opioid regimen, thereby mitigating the risk of addiction.

The project is part of a $50 million initiative called Untangling Addiction, funded by Wellcome Leap. The initiative aims to revolutionize our understanding of opioid addiction and use innovative tools like AI and predictive modeling to intervene. The University of California San Diego School of Medicine is one of 14 institutions worldwide to receive funding for this initiative.

Generative AI for Predicting Opioid Addiction

The AI model being developed uses generative artificial intelligence (GenAI), which can produce various types of content. This approach provides a more comprehensive understanding and prediction of a patient’s past and future behaviors. The goal is to predict the risk of addiction from the moment a patient is prescribed an opioid to the moment they start to become addicted.

The model will leverage large multi-institutional datasets to incorporate genomic, social determinants of health, clinical, procedural, and demographic data. This data will be used to predict the development of opioid use disorder and related outcomes among any patient initially prescribed an opioid.

Role of Anesthesiologists in Opioid Addiction Prevention

Anesthesiologists play a crucial role in preventing opioid addiction. They have access to a variety of secure data, which they review to safely guide a patient through surgery. The research focus is on how AI-assisted knowledge of a patient’s risks can optimize their overall care and decrease the chances of addiction.

The insights gained from this project will be applicable to many other areas of a patient’s healthcare journey, resulting in better outcomes and care. When the predictive tool is ready for testing in clinical settings, the research team will partner with the Joan & Irwin Jacobs Center for Health Innovation at UC San Diego Health (JCHI), which provides a unique environment for integrating AI approaches into clinical care.

Real-World Evaluations of GenAI’s Potential

For Karandeep Singh, MD, the inaugural chief health AI officer at UC San Diego Health, real-world evaluations of GenAI’s potential are critical. Generative AI has the potential to help us better understand people’s risk, but this idea hasn’t really been put to the test in most areas of medicine. This project will be key towards helping us understand the potential of generative AI in identifying opioid risk.

Commercially Available Genomic and Microbiome Panel

The ultimate goal of the project is to develop a commercially available genomic and microbiome panel that clinicians can use to easily assess opioid addiction. The project also aims to develop automated approaches using AI to integrate into electronic health record systems. This will allow for real-time predictions of risk throughout a patient’s entirety of care and lead the way in the prevention of opioid addiction.